This course runs for a duration of 4 Days.
The class will run daily from 8 AM CT to 4 PM CT.
Class Location: Virtual LIVE Instructor Led - Virtual Live Classroom.
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.
Course Objectives
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
Who Should Attend?
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
1 - Explore Azure Machine Learning workspace resources and assets
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace
2 - Explore developer tools for workspace interaction
Explore the studio
Explore the Python SDK
Explore the CLI
3 - Make data available in Azure Machine Learning
Understand URIs
Create a datastore
Create a data asset
4 - Work with compute targets in Azure Machine Learning
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster
5 - Work with environments in Azure Machine Learning
Understand environments
Explore and use curated environments
Create and use custom environments
6 - Find the best classification model with Automated Machine Learning
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models
7 - Track model training in Jupyter notebooks with MLflow
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
8 - Run a training script as a command job in Azure Machine Learning
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job
9 - Track model training with MLflow in jobs
Track metrics with MLflow
View metrics and evaluate models
10 - Perform hyperparameter tuning with Azure Machine Learning
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning
11 - Run pipelines in Azure Machine Learning
Create components
Create a pipeline
Run a pipeline job
12 - Register an MLflow model in Azure Machine Learning
Log models with MLflow
Understand the MLflow model format
Register an MLflow model
13 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Understand Responsible AI
Create the Responsible AI dashboard
Evaluate the Responsible AI dashboard
14 - Deploy a model to a managed online endpoint
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints
15 - Deploy a model to a batch endpoint
Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints
16 - Introduction to Azure AI Foundry
What is Azure AI Foundry?
How does Azure AI Foundry work
When to use Azure AI Foundry
17 - Explore and deploy models from the model catalog in Azure AI Foundry portal
Explore the language models in the model catalog
Deploy a model to an endpoint
Improve the performance of a language model
18 - Get started with prompt flow to develop language model apps in the Azure AI Foundry
Understand the development lifecycle of a large language model (LLM) app
Understand core components and explore flow types
Explore connections and runtimes
Explore variants and monitoring options
19 - Build a RAG-based agent with your own data using Azure AI Foundry
Understand how to ground your language model
Make your data searchable
Build an agent with prompt flow
20 - Fine-tune a language model with Azure AI Foundry
Understand when to fine-tune a language model
Prepare your data to fine-tune a chat completion model
Explore fine-tuning language models in Azure AI Studio
21 - Evaluate the performance of generative AI apps with Azure AI Foundry
Assess the model performance
Manually evaluate the performance of a model
Assess the performance of your generative AI apps
22 - Responsible generative AI
Plan a responsible generative AI solution
Identify potential harms
Measure potential harms
Mitigate potential harms
Operate a responsible generative AI solution
